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Enchondroma Detection from Hand Radiographs with an Interactive Deep Learning Segmentation Tool—A Feasibility Study
Enchondromas are common benign bone tumors, usually presenting in the hand. They can cause symptoms such as swelling and pain but often go un-noticed. If the tumor expands, it can diminish the bone cortices and predispose the bone to fracture. Diagnosis is based on clinical investigation and radiogr...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10672653/ https://www.ncbi.nlm.nih.gov/pubmed/38002741 http://dx.doi.org/10.3390/jcm12227129 |
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author | Anttila, Turkka Tapio Aspinen, Samuli Pierides, Georgios Haapamäki, Ville Laitinen, Minna Katariina Ryhänen, Jorma |
author_facet | Anttila, Turkka Tapio Aspinen, Samuli Pierides, Georgios Haapamäki, Ville Laitinen, Minna Katariina Ryhänen, Jorma |
author_sort | Anttila, Turkka Tapio |
collection | PubMed |
description | Enchondromas are common benign bone tumors, usually presenting in the hand. They can cause symptoms such as swelling and pain but often go un-noticed. If the tumor expands, it can diminish the bone cortices and predispose the bone to fracture. Diagnosis is based on clinical investigation and radiographic imaging. Despite their typical appearance on radiographs, they can primarily be misdiagnosed or go totally unrecognized in the acute trauma setting. Earlier applications of deep learning models to image classification and pattern recognition suggest that this technique may also be utilized in detecting enchondroma in hand radiographs. We trained a deep learning model with 414 enchondroma radiographs to detect enchondroma from hand radiographs. A separate test set of 131 radiographs (47% with an enchondroma) was used to assess the performance of the trained deep learning model. Enchondroma annotation by three clinical experts served as our ground truth in assessing the deep learning model’s performance. Our deep learning model detected 56 enchondromas from the 62 enchondroma radiographs. The area under receiver operator curve was 0.95. The F1 score for area statistical overlapping was 69.5%. Our deep learning model may be a useful tool for radiograph screening and raising suspicion of enchondroma. |
format | Online Article Text |
id | pubmed-10672653 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106726532023-11-16 Enchondroma Detection from Hand Radiographs with an Interactive Deep Learning Segmentation Tool—A Feasibility Study Anttila, Turkka Tapio Aspinen, Samuli Pierides, Georgios Haapamäki, Ville Laitinen, Minna Katariina Ryhänen, Jorma J Clin Med Article Enchondromas are common benign bone tumors, usually presenting in the hand. They can cause symptoms such as swelling and pain but often go un-noticed. If the tumor expands, it can diminish the bone cortices and predispose the bone to fracture. Diagnosis is based on clinical investigation and radiographic imaging. Despite their typical appearance on radiographs, they can primarily be misdiagnosed or go totally unrecognized in the acute trauma setting. Earlier applications of deep learning models to image classification and pattern recognition suggest that this technique may also be utilized in detecting enchondroma in hand radiographs. We trained a deep learning model with 414 enchondroma radiographs to detect enchondroma from hand radiographs. A separate test set of 131 radiographs (47% with an enchondroma) was used to assess the performance of the trained deep learning model. Enchondroma annotation by three clinical experts served as our ground truth in assessing the deep learning model’s performance. Our deep learning model detected 56 enchondromas from the 62 enchondroma radiographs. The area under receiver operator curve was 0.95. The F1 score for area statistical overlapping was 69.5%. Our deep learning model may be a useful tool for radiograph screening and raising suspicion of enchondroma. MDPI 2023-11-16 /pmc/articles/PMC10672653/ /pubmed/38002741 http://dx.doi.org/10.3390/jcm12227129 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Anttila, Turkka Tapio Aspinen, Samuli Pierides, Georgios Haapamäki, Ville Laitinen, Minna Katariina Ryhänen, Jorma Enchondroma Detection from Hand Radiographs with an Interactive Deep Learning Segmentation Tool—A Feasibility Study |
title | Enchondroma Detection from Hand Radiographs with an Interactive Deep Learning Segmentation Tool—A Feasibility Study |
title_full | Enchondroma Detection from Hand Radiographs with an Interactive Deep Learning Segmentation Tool—A Feasibility Study |
title_fullStr | Enchondroma Detection from Hand Radiographs with an Interactive Deep Learning Segmentation Tool—A Feasibility Study |
title_full_unstemmed | Enchondroma Detection from Hand Radiographs with an Interactive Deep Learning Segmentation Tool—A Feasibility Study |
title_short | Enchondroma Detection from Hand Radiographs with an Interactive Deep Learning Segmentation Tool—A Feasibility Study |
title_sort | enchondroma detection from hand radiographs with an interactive deep learning segmentation tool—a feasibility study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10672653/ https://www.ncbi.nlm.nih.gov/pubmed/38002741 http://dx.doi.org/10.3390/jcm12227129 |
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